Artesunate acts through cytochrome c to inhibit growth of pediatric AML cells

Artesunate is a derivative of artemisinin, an active compound isolated from Artemisia annua which has been used in Traditional Chinese Medicine and to treat malaria worldwide. Artemisinin derivatives have exhibited anti-cancer activity against both solid tumors and leukemia. The direct target(s) of artesunate are controversial; although, heme-bound proteins in the mitochondria have been implicated. We utilized computational modeling to calculate the predicted binding score of artesunate with heme-bound mitochondrial proteins and identified cytochrome c as potential artesunate target. UV-visible spectroscopy showed changes in the absorbance spectrum, and thus protein structure, when cytochrome c was incubated with artesunate. Artesunate induces apoptosis, disrupts mitochondrial membrane potential, and is antagonized by methazolamide in pediatric AML cells indicating a probable mechanism of action involving cytochrome c. We utilized a multi-disciplinary approach to show that artesunate can interact with and is dependent on cytochrome c release to induce cell death in pediatric AML cell lines.

Open Data and transparency in artificial intelligence and machine learning: A new era of research

Artificial Intelligence (AI) and machine learning are the current forefront of computer science and technology. AI and related sub-disciplines, including machine learning, are essential technologies which have enabled the widespread use of smart technology, such as smart phones, smart home appliances and even electric toothbrushes. It is AI that allows the devices used day-to-day across people's personal lives, working lives and in industry to better anticipate and respond to our needs. However, the use of AI technology comes with a range of ethical questions - including issues around privacy, security, reliability, copyright/plagiarism and whether AI is capable of independent, conscious thought. We have seen several issues related to racial and sexual bias in AI in the recent times, putting the reliability of AI in question. Many of these issues have been brought to the forefront of cultural awareness in late 2022, early 2023, with the rise of AI art programs (and the copyright issues arising from the deep-learning methods employed to train this AI), and the popularity of ChatGPT alongside its ability to be used to mimic human output, particularly in regard to academic work. In critical areas like healthcare, the errors of AI can be fatal. With the incorporation of AI in almost every sector of our everyday life, we need to keep asking ourselves- can we trust AI, and how much? This Editorial outlines the importance of openness and transparency in the development and applications of AI to allow all users to fully understand both the benefits and risks of this ubiquitous technology, and outlines how the Artificial Intelligence and Machine Learning  Gateway on F1000Research meets these needs.

The RelB-BLNK Axis Determines Cellular Response to a Novel Redox-Active Agent Betamethasone during Radiation Therapy in Prostate Cancer

Aberrant levels of reactive oxygen species (ROS) are potential mechanisms that contribute to both cancer therapy efficacy and the side effects of cancer treatment. Upregulation of the non-canonical redox-sensitive NF-kB family member, RelB, confers radioresistance in prostate cancer (PCa). We screened FDA-approved compounds and identified betamethasone (BET) as a drug that increases hydrogen peroxide levels in vitro and protects non-PCa tissues/cells while also enhancing radiation killing of PCa tissues/cells, both in vitro and in vivo. Significantly, BET increases ROS levels and exerts different effects on RelB expression in normal cells and PCa cells. BET induces protein expression of RelB and RelB target genes, including the primary antioxidant enzyme, manganese superoxide dismutase (MnSOD), in normal cells, while it suppresses protein expression of RelB and MnSOD in LNCaP cells and PC3 cells. RNA sequencing analysis identifies B-cell linker protein (BLNK) as a novel RelB complementary partner that BET differentially regulates in normal cells and PCa cells. RelB and BLNK are upregulated and correlate with the aggressiveness of PCa in human samples. The RelB-BLNK axis translocates to the nuclear compartment to activate MnSOD protein expression. BET promotes the RelB-BLNK axis in normal cells but suppresses the RelB-BLNK axis in PCa cells. Targeted disruptions of RelB-BLNK expressions mitigate the radioprotective effect of BET on normal cells and the radiosensitizing effect of BET on PCa cells. Our study identified a novel RelB complementary partner and reveals a complex redox-mediated mechanism showing that the RelB-BLNK axis, at least in part, triggers differential responses to the redox-active agent BET by stimulating adaptive responses in normal cells but pushing PCa cells into oxidative stress overload.

KEAP1 Is Required for Artesunate Anticancer Activity in Non-Small-Cell Lung Cancer

Artesunate is the most common treatment for malaria throughout the world. Artesunate has anticancer activity likely through the induction of reactive oxygen species, the same mechanism of action utilized in Plasmodium falciparum infections. Components of the kelch-like ECH-associated protein 1 (KEAP1)/nuclear factor erythroid 2-related factor 2 (NRF2) pathway, which regulates cellular response to oxidative stress, are mutated in approximately 30% of non-small-cell lung cancers (NSCLC); therefore, we tested the hypothesis that KEAP1 is required for artesunate sensitivity in NSCLC. Dose response assays identified A549 cells, which have a G333C-inactivating mutation in KEAP1, as resistant to artesunate, with an IC50 of 23.6 µM, while H1299 and H1563 cells were sensitive to artesunate, with a 10-fold lower IC50. Knockdown of KEAP1 through siRNA caused increased resistance to artesunate in H1299 cells. Alternatively, the pharmacological inhibition of NRF2, which is activated downstream of KEAP1 loss, by ML385 partially restored sensitivity of A549 cells to artesunate, and the combination of artesunate and ML385 was synergistic in both A549 and H1299 cells. These findings demonstrate that KEAP1 is required for the anticancer activity of artesunate and support the further development of NRF2 inhibitors to target patients with mutations in the KEAP1/NRF2 pathway.

Machine learning and ligand binding predictions: A review of data, methods, and obstacles

Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.

Characterization of Squamous Cell Lung Cancers from Appalachian Kentucky

BACKGROUND

Lung cancer is the leading cause of cancer mortality in the United States (U.S.). Squamous cell carcinoma (SQCC) represents 22.6% of all lung cancers nationally, and 26.4% in Appalachian Kentucky (AppKY), where death from lung cancer is exceptionally high. The Cancer Genome Atlas (TCGA) characterized genetic alterations in lung SQCC, but this cohort did not focus on AppKY residents.

METHODS

Whole-exome sequencing was performed on tumor and normal DNA samples from 51 lung SQCC subjects from AppKY. Somatic genomic alterations were compared between the AppKY and TCGA SQCC cohorts.

RESULTS

From this AppKY cohort, we identified an average of 237 nonsilent mutations per patient and, in comparison with TCGA, we found that PCMTD1 (18%) and IDH1 (12%) were more commonly altered in AppKY versus TCGA. Using IDH1 as a starting point, we identified a mutually exclusive mutational pattern (IDH1, KDM6A, KDM4E, JMJD1C) involving functionally related genes. We also found actionable mutations (10%) and/or intermediate or high-tumor mutation burden (65%), indicating potential therapeutic targets in 65% of subjects.

CONCLUSIONS

This study has identified an increased percentage of IDH1 and PCMTD1 mutations in SQCC arising in the AppKY residents versus TCGA, with population-specific implications for the personalized treatment of this disease.

IMPACT

Our study is the first report to characterize genomic alterations in lung SQCC from AppKY. These findings suggest population differences in the genetics of lung SQCC between AppKY and U.S. populations, highlighting the importance of the relevant population when developing personalized treatment approaches for this disease.

Polypharmacology Within the Full Kinome: a Machine Learning Approach

Protein kinases generate nearly a thousand different protein products and regulate the majority of cellular pathways and signal transduction. It is therefore not surprising that the deregulation of kinases has been implicated in many disease states. In fact, kinase inhibitors are the largest class of new cancer therapies. Understanding polypharmacology within the full kinome, how drugs interact with many different kinases, would allow for the development of safer and more efficacious cancer therapies. A full understanding of these interactions is not experimentally feasible making highly accurate computational predictions extremely useful and important. This work aims at making a machine learning model useful for investigating the full kinome. We evaluate many feature sets for our model and get better performance over molecular docking with all of them. We demonstrate that you can achieve a nearly 60% increase in success rate at identifying binding compounds using our model over molecular docking scores.

Incorporating Protein Dynamics Through Ensemble Docking in Machine Learning Models to Predict Drug Binding

Drug discovery is an expensive, lengthy, and sometimes dangerous process. The ability to make accurate computational predictions of drug binding would greatly improve the cost-effectiveness and safety of drug discovery and development. This study incorporates ensemble docking, the use of multiple protein conformations extracted from a molecular dynamics trajectory to perform docking calculations, with additional biomedical data sources and machine learning algorithms to improve the prediction of drug binding. We found that we can greatly increase the classification accuracy of an active vs a decoy compound using these methods over docking scores alone. The best results seen here come from having an individual protein conformation that produces binding features that correlate well with the active vs. decoy classification, in which case we achieve over 99% accuracy. The ability to confidently make accurate predictions on drug binding would allow for computational polypharamacological networks with insights into side-effect prediction, drug-repurposing, and drug efficacy.

Genetic Comparison of Symptomatic and Asymptomatic Persons With Alzheimer Disease Neuropathology

OBJECTIVE

The objective was to determine whether symptomatic and asymptomatic persons with Alzheimer disease (AD) neuropathology have different allele counts for single-nucleotide polymorphisms that have been associated with clinical late-onset AD.

METHODS

Data came from the National Alzheimer's Coordinating Center Uniform Data Set and Neuropathology Data Set, and the Alzheimer's Disease Genetics Consortium (ADGC). Participants had low to high AD neuropathologic change. The 22 known/suspected genes associated with late-onset AD were considered. "Symptomatic" was defined as Clinical Dementia Rating global score >0.

RESULTS

Sixty-eight asymptomatic and 521 symptomatic participants met inclusion criteria. Single-nucleotide polymorphisms associated with ABCA7 [odds ratio (OR)=1.66; 95% confidence interval (CI), 1.03-2.85] and MAPT (OR=2.18; CI, 1.26-3.77) were associated with symptomatic status. In stratified analyses, loci containing CD2AP (OR=0.35; 95% CI, 0.16-0.74), ZCWPW1 (OR=2.98; 95% CI, 1.34-6.86), and MAPT (OR=3.73, 95% CI, 1.30-11.76) were associated with symptomatic status in APOE e4 carriers.

CONCLUSIONS

These findings potentially explain some of the variation in whether a person with AD neuropathology expresses symptoms. Understanding why some people remain cognitively normal despite having AD neuropathology could identify pathways to disease heterogeneity and guide treatment trials.

Gene-based association study of genes linked to hippocampal sclerosis of aging neuropathology: GRN, TMEM106B, ABCC9, and KCNMB2

Hippocampal sclerosis of aging (HS-Aging) is a common neurodegenerative condition associated with dementia. To learn more about genetic risk of HS-Aging pathology, we tested gene-based associations of the GRN, TMEM106B, ABCC9, and KCNMB2 genes, which were reported to be associated with HS-Aging pathology in previous studies. Genetic data were obtained from the Alzheimer's Disease Genetics Consortium, linked to autopsy-derived neuropathological outcomes from the National Alzheimer's Coordinating Center. Of the 3251 subjects included in the study, 271 (8.3%) were identified as an HS-Aging case. The significant gene-based association between the ABCC9 gene and HS-Aging appeared to be driven by a region in which a significant haplotype-based association was found. We tested this haplotype as an expression quantitative trait locus using 2 different public-access brain gene expression databases. The HS-Aging pathology protective ABCC9 haplotype was associated with decreased ABCC9 expression, indicating a possible toxic gain of function.

Proceedings of the 15th Annual UT-KBRIN Bioinformatics Summit 2016

I1 Proceedings of the Fifteenth Annual UT- KBRIN Bioinformatics Summit 2016

Eric C. Rouchka, Julia H. Chariker, Benjamin J. Harrison, Juw Won Park

P1 CC-PROMISE: Projection onto the Most Interesting Statistical Evidence (PROMISE) with Canonical Correlation to integrate gene expression and methylation data with multiple pharmacologic and clinical endpoints

Xueyuan Cao, Stanley Pounds, Susana Raimondi, James Downing, Raul Ribeiro, Jeffery Rubnitz, Jatinder Lamba

P2 Integration of microRNA-mRNA interaction networks with gene expression data to increase experimental power

Bernie J Daigle, Jr.

P3 Designing and writing software for in silico subtractive hybridization of large eukaryotic genomes

Deborah Burgess, Stephanie Gehrlich, John C Carmen

P4 Tracking the molecular evolution of Pax gene

Nicholas Johnson; Chandrakanth Emani

P5 Identifying genetic differences in thermally dimorphic and state specific fungi using in silico genomic comparison

Stephanie Gehrlich, Deborah Burgess, John C Carmen

P6 Identification of conserved genomic regions and variation therein amongst Cetartiodactyla species using next generation sequencing

Kalpani De Silva, Michael P Heaton, Theodore S Kalbfleisch

P7 Mining physiological data to identify patients with similar medical events and phenotypes

Teeradache Viangteeravat, Rahul Mudunuri, Oluwaseun Ajayi, Fatih Şen, Eunice Y Huang

P8 Smart brief for home health monitoring

Mohammad Mohebbi, Luaire Florian, Douglas J Jackson, John F Naber

P9 Side-effect term matching for computational adverse drug reaction predictions

AKM Sabbir, Sally R Ellingson

P10 Enrichment vs robustness: A comparison of transcriptomic data clustering metrics

Yuping Lu, Charles A Phillips, Michael A Langston

P11 Deep neural networks for transcriptome-based cancer classification

Rahul K Sevakula, Raghuveer Thirukovalluru, Nishchal K. Verma, Yan Cui

P12 Motif discovery using K-means clustering

Mohammed Sayed, Juw Won Park

P13 Large scale discovery of active enhancers from nascent RNA sequencing

Jing Wang, Qi Liu, Yu Shyr

P14 Computationally characterizing genomic pipelines and benchmarking results using GATK best practices on the high performance computing cluster at the University of Kentucky

Xiaofei Zhang, Sally R Ellingson

P15 Development of approaches enabling the identification of abnormal gene expression from RNA-Seq in personalized oncology

Naresh Prodduturi, Gavin R Oliver, Diane Grill, Jie Na, Jeanette Eckel-Passow, Eric W Klee

P16 Processing RNA-Seq data of plants infected with coffee ringspot virus

Michael M Goodin, Mark Farman, Harrison Inocencio, Chanyong Jang, Jerzy W Jaromczyk, Neil Moore, Kelly Sovacool

P17 Comparative transcriptomics of three Acinetobacter baumanii clinical isolates with different antibiotic resistance patterns

Leon Dent, Mike Izban, Sammed Mandape, Shruti Sakhare, Siddharth Pratap, Dana Marshall

P18 Metagenomic assessment of possible microbial contamination in the equine reference genome assembly

M Scotty DePriest, James N MacLeod, Theodore S Kalbfleisch

P19 Molecular evolution of cancer driver genes

Chandrakanth Emani, Hanady Adam, Ethan Blandford, Joel Campbell, Joshua Castlen, Brittany Dixon, Ginger Gilbert, Aaron Hall, Philip Kreisle, Jessica Lasher, Bethany Oakes, Allison Speer, Maximilian Valentine

P20 Biorepository Laboratory Information Management System

Naga Satya V Rao Nagisetty, Rony Jose, Teeradache Viangteeravat, Robert Rooney, David Hains

Automated quality control for genome wide association studies

This paper provides details on the necessary steps to assess and control data in genome wide association studies (GWAS) using genotype information on a large number of genetic markers for large number of individuals. Due to varied study designs and genotyping platforms between multiple sites/projects as well as potential genotyping errors, it is important to ensure high quality data. Scripts and directions are provided to facilitate others in this process.

Ensemble-based docking: From hit discovery to metabolism and toxicity predictions

This paper describes and illustrates the use of ensemble-based docking, i.e., using a collection of protein structures in docking calculations for hit discovery, the exploration of biochemical pathways and toxicity prediction of drug candidates. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials.

Multi-conformer ensemble docking to difficult protein targets

Large-scale ensemble docking is investigated using five proteins from the Directory of Useful Decoys (DUD, dud.docking.org ) for which docking to crystal structures has proven difficult. Molecular dynamics trajectories are produced for each protein and an ensemble of representative conformational structures extracted from the trajectories. Docking calculations are performed on these selected simulation structures and ensemble-based enrichment factors compared with those obtained using docking in crystal structures of the same protein targets or random selection of compounds. Simulation-derived snapshots are found with improved enrichment factors that increase the chemical diversity of docking hits for four of the five selected proteins. A combination of all the docking results obtained from molecular dynamics simulation followed by selection of top-ranking compounds appears to be an effective strategy for increasing the number and diversity of hits when using docking to screen large libraries of chemicals against difficult protein targets.

Accelerating Virtual High-Throughput Ligand Docking: current technology and case study on a petascale supercomputer

In this paper we give the current state of high-throughput virtual screening. We describe a case study of using a task-parallel MPI (Message Passing Interface) version of Autodock4 [1], [2] to run a virtual high-throughput screen of one-million compounds on the Jaguar Cray XK6 Supercomputer at Oak Ridge National Laboratory. We include a description of scripts developed to increase the efficiency of the predocking file preparation and postdocking analysis. A detailed tutorial, scripts, and source code for this MPI version of Autodock4 are available online at http://www.bio.utk.edu/baudrylab/autodockmpi.htm.

Detection of Bacillus anthracis DNA in complex soil and air samples using next-generation sequencing

Bacillus anthracis is the potentially lethal etiologic agent of anthrax disease, and is a significant concern in the realm of biodefense. One of the cornerstones of an effective biodefense strategy is the ability to detect infectious agents with a high degree of sensitivity and specificity in the context of a complex sample background. The nature of the B. anthracis genome, however, renders specific detection difficult, due to close homology with B. cereus and B. thuringiensis. We therefore elected to determine the efficacy of next-generation sequencing analysis and microarrays for detection of B. anthracis in an environmental background. We applied next-generation sequencing to titrated genome copy numbers of B. anthracis in the presence of background nucleic acid extracted from aerosol and soil samples. We found next-generation sequencing to be capable of detecting as few as 10 genomic equivalents of B. anthracis DNA per nanogram of background nucleic acid. Detection was accomplished by mapping reads to either a defined subset of reference genomes or to the full GenBank database. Moreover, sequence data obtained from B. anthracis could be reliably distinguished from sequence data mapping to either B. cereus or B. thuringiensis. We also demonstrated the efficacy of a microbial census microarray in detecting B. anthracis in the same samples, representing a cost-effective and high-throughput approach, complementary to next-generation sequencing. Our results, in combination with the capacity of sequencing for providing insights into the genomic characteristics of complex and novel organisms, suggest that these platforms should be considered important components of a biosurveillance strategy.

Known and novel post-transcriptional regulatory sequences are conserved across plant families

The sequence elements that mediate post-transcriptional gene regulation often reside in the 5' and 3' untranslated regions (UTRs) of mRNAs. Using six different families of dicotyledonous plants, we developed a comparative transcriptomics pipeline for the identification and annotation of deeply conserved regulatory sequences in the 5' and 3' UTRs. Our approach was robust to confounding effects of poor UTR alignability and rampant paralogy in plants. In the 3' UTR, motifs resembling PUMILIO-binding sites form a prominent group of conserved motifs. Additionally, Expansins, one of the few plant mRNA families known to be localized to specific subcellular sites, possess a core conserved RCCCGC motif. In the 5' UTR, one major subset of motifs consists of purine-rich repeats. A distinct and substantial fraction possesses upstream AUG start codons. Half of the AUG containing motifs reveal hidden protein-coding potential in the 5' UTR, while the other half point to a peptide-independent function related to translation. Among the former, we added four novel peptides to the small catalog of conserved-peptide uORFs. Among the latter, our case studies document patterns of uORF evolution that include gain and loss of uORFs, switches in uORF reading frame, and switches in uORF length and position. In summary, nearly three hundred post-transcriptional elements show evidence of purifying selection across the eudicot branch of flowering plants, indicating a regulatory function spanning at least 70 million years. Some of these sequences have experimental precedent, but many are novel and encourage further exploration.

Ammonia permeability of the soybean nodulin 26 channel

Soybean nodulin 26 (nod26), a member of the aquaporin superfamily, is the major protein component of the symbiosome membrane that encloses nitrogen-fixing bacteroids in root nodules. Previous work has demonstrated that nod26 facilitates the transport of water and glycerol, although a potential additional role as a channel for fixed ammonia efflux has been hypothesized. In the present study it is shown that recombinant nod26 reconstituted into proteoliposomes facilitates NH(3) transport in an Hg(2+)-sensitive manner with a reduced activation energy, hallmarks of protein-facilitated transport characteristic of aquaporins. Comparison of the predicted single-channel transport rates of nod26 suggests a 4.9-fold preference for ammonia compared to water.